A critical limitation in the area of disease identification, diagnosis, and prevention has been the lack of simple, rapid, and effective screening techniques. This problem is particularly acute in locations and/or situations where rapid analysis and diagnosis may involve decisions concerning life-threatening circumstances such as natural disasters or combat, and where the need for portable laboratories is accentuated by the remoteness of areas where diseases are endemic and where epidemics are generated. In addition, in the medical field there is a considerable need for the identification of markers that permit the diagnosis and treatment of diseases early in their development stage and thus avoid lengthy periods of incubation, which invariably worsen the condition of the patient.
Typically, microorganisms and viruses of concern have sizes ranging between 0.5 and 20 pm and, in many cases, are present in fairly dilute concentrations. Although the analytical instrumentation used in medical and clinical laboratories has improved considerably over the past decade to the present, there are still no suitable techniques capable of detecting, classifying, and counting microorganisms in bodily fluids.
Technology known in the art requires that the presence of target microorganisms be detected using microscopy and/or immunoassay techniques. These require a significant amount of time, trained technicians, and well-equipped laboratory facilities.
The costs associated with current laboratory techniques for disease identification and diagnosis therefore further accentuate the need for the development of rapid screening methods.
Another limitation of the currently employed technology is a lack of on-line capability and continuous measurement capabilities for the characterization of blood and other fluid components, as well as a lack of portable instrumentation capable of detecting, counting, and classifying specific blood and other fluid components. The problem of portable instrumentation and suitable methods of analysis and diagnosis is particularly relevant to the medical industry, where the need for rapid analysis and diagnosis often involves life-threatening situations. Although the analytical instrumentation used in medical and clinical laboratories has improved considerably over the past decade, there are still no suitable techniques capable of detecting, classifying, and counting on-line critical cell populations and/or pathogens in blood and other bodily fluids.
Blood cell component counting technology known in the art uses, for example, red cell counts, platelet counts, and white cell counts as indicators of the state of disease. White blood cells can be difficult to count if they are present in small numbers. At present automated hematology analyzers that employ light scattering or impedance techniques are used, but these can introduce a high error rate when determining counts for low sample numbers. In cases of leukoreduced blood products with lower numbers of white blood cells, staining and microscopy or flow cytometry are typically used.
As is known from spectroscopy theory, a measure of the absorption of the attenuation of light through a solution or a suspension is the extinction coefficient, which also provides a measure of the turbidity and transmission properties of a sample. Spectra in the visible region of the electromagnetic spectrum reflect the presence of metal ions and large conjugated aromatic structures and double-bond systems. In the near-ultraviolet (uv) region small conjugated ring systems affect absorption properties. However, suspensions of very large particles are powerful scatterers of radiation, and in the case of cells and microorganisms, the light scattering effect can be sufficiently strong to overwhelm absorption effects. It is therefore known to use uv-vis spectroscopy to monitor purity, concentration, and reaction rates of such large particles and their suspending media.
The detection of trace amounts of albumin in urine has been developed into an important tool for the diagnosis and monitoring of renal and heart diseases. The main difficulty in the detection of albumin and other proteins arises from the relatively weak absorption coefficients of the protein chromophoric amino acids. This requires either high concentrations for detection, or the incorporation of stronger chromophores or fluorophores to enable spectrophotometric detection. Alternatively, the use of antibodies attached to micro-spheres is also a means of collecting the protein and enabling detection. Although existing methods have the required sensitivity (reproducible detection down to 10 mg/L) they are expensive and in many instances semi quantitative only.
Many attempts have been made to estimate the particle size distribution (PSD) and the chemical composition of suspended particles using optical spectral extinction (transmission) measurements. However, previously used techniques neglect the effects of the chemical composition and require that either the form of the P80 be known a priori or that the shape of the PSD be assumed. One of the present inventors has applied standard regularization techniques to the solution of the transmission equation and has demonstrated correct PSDs of a large variety of polymer lathces, protein aggregates, silicon dioxide and alumina particles, and microorganisms.
It has also been known to use the complementary information available from simultaneous absorption and light scattering measurements at multiple angles for the characterization of the composition and molecular weight and shape of macromolecules and suspended particles (Garcia-Rubio, 1993; and U.S. Pat. No. 5,808,738), the disclosure of which is incorporated herein by reference.
Interferometric techniques are known in the art for cell classification (Cabib et at., U.S. Pat. Nos. 5,991,028 and 5,784,162) which use fluorescence microscopy with stained cells. Fluorescence and reflection spectroscopy can also be used to characterize a material by sensing a single wavelength (Lemelson, U.S. Pat. Nos. 5,995,866; 5,735,276; and 5,948,272), which can detect organisms in a bodily fluid. Electroluminescence may also be used to detect an analyte in a sample (Massey et at., U.S. Pat. No. 5,935,779). Cell counting may be accomplished by vibrational spectroscopy (Zakim et al. U.S. Pat. No. 5,733,739). Infrared techniques can detect cellular abnormalities (Cohenford et al., U.S. Pat. Nos. 6,146,897 and 5,976,885; Sodickson et al., U.S. Pat. No. 6,028,311).
One of the present inventors previously developed ultraviolet-visible spectroscopic techniques for detecting and classifying microorganisms in water (Garcia Rubio, U.S. Pat. No. 5,616,457), for characterizing blood and blood types (Garcia Rublo, U.S. Pat. No. 5,589,932), and, as mentioned above, for characterizing particles with a multiangle-multiwavelength system (Garcia-Rubio et at., U.S. Pat. No. 5,808,738). The disclosures of these patents are incorporated herein by reference.
The optical spectral extinction of particle dispersions (combined absorption and scattering characteristics) contains information that, in principle, can be used to estimate the size distribution (PSD) and the chemical composition of the suspended particles. This extinction as a function of wavelength can be obtained from a variety of optical configurations including transmission measurements (Bohren and Huffman, 1983), angular scattering measurements (van de Hulst, 1969) and through the use of integrating spheres (Kortum, 1969). A large number of techniques for the estimation of the PSD from turbidity measurements have been reported (van de Hulst, 1957; Kerker, 1969; Wallach et al., 1961; Zollars, 1980; Melik and Fogler, 1983). Most of these techniques require that either the form of the particle size distribution be known a priori, or that the shape of the PSD be assumed (Zollars, 1980, Melik and Fogler, 1983). Regularization techniques, which make no assumptions regarding the shape of the particle size distribution (Towmey, 1979; Golub et al., 1979; Tarantola, 1987), have been applied to the interpretation of transmission spectra (Elicabe and Garcia-Rubio, 1990, 1988). The regularized solution has been shown to yield the correct particle size distribution and the chemical composition for a large variety of polymer lattices (Brandolin and Garcia-Rubio, 1991), protein aggregates (Garcia-Rubio et al. 1993), SiO2 particles (Koumarioti et al, 1999) and whole blood and blood components (Mattley et al. 2000, Garcia-Rubio, unpublished data). The equation that relates the turbidity (τ(λ0)) measured at a given wavelength λ0 and the normalized particle size distribution for spherical particles (f(D)) is given by (Van de Hulst, 1957, Kerker, 1969):
                              τ          ⁡                      (                          λ              0                        )                          =                              Np            ⁡                          (                              π                4                            )                                ⁢                                    ∫              0              ∞                        ⁢                                                            Q                  ext                                ⁡                                  (                                                            m                      ⁡                                              (                                                  λ                          0                                                )                                                              ,                    D                                    )                                            ⁢                              D                2                            ⁢                              f                ⁡                                  (                  d                  )                                            ⁢                                                          ⁢                              ⅆ                D                                                                        (        1        )            
Where D is the effective particle diameter, Qext(m(λ0),D) corresponds to Mie extinction coefficient, and Np is the number of particles per unit volume. The Mie extinction coefficient is a function of the optical properties of the particles and suspending medium through the complex refractive index (m(λ0)) given in Equation 2.
                              m          ⁡                      (                          λ              0                        )                          =                                            n              ⁡                              (                                  λ                  0                                )                                      +                          i              ⁢                                                          ⁢                              κ                ⁡                                  (                                      λ                    0                                    )                                                                                        n              0                        ⁡                          (                              λ                0                            )                                                          (        2        )            where n(λ0) and n0(λ0) correspond to the refractive index of the particles and the suspending medium, respectively. The absorption coefficient of the suspended particles is represented by κ(λ0).
Equation 1 can be written in matrix form by discretizing the integral with an appropriate quadrature approximation (Elicabe and Garcia-Rubio, 1990; Elicabe and Garcia-Rubio, 1988):
                              τ          _                =                              A            ⁢                          f              _                                +                      ɛ            _                                              (        3        )            
Where a composite of experimental errors is represented, errors due to the model approximations and the errors introduced by the discretization procedure (Elicabe and Garcia-Rubio, 1990). Equation 4 gives the regularized solution to Equation 3:
                                                        f              _                        ^                    ⁡                      (            γ            )                          =                                            (                                                                    A                    T                                    ⁢                  A                                +                                  γ                  ⁢                                                                          ⁢                  H                                            )                                      -              1                                ⁢                      A            T                    ⁢                      τ            _                                              (        4        )            
Where H is a covariance matrix that essentially filters the experimental and the approximation errors; the regularization parameter is estimated using the Generalized Cross-Validation technique (GCV) (Golub et al., 1979). The Generalized Cross-Validation technique requires the minimization of Equation 5 with respect to the regularization parameter (Golub et al., 1979; Elicabe and Garcia-Rubio, 1990):
                              V          ⁡                      (            γ            )                          =                  Nob          ⁢                                                                                    (                                      I                    -                                                                                            A                          ⁡                                                      (                                                                                                                            A                                  T                                                                ⁢                                A                                                            +                                                              γ                                ⁢                                                                                                                                  ⁢                                H                                                                                      )                                                                                                    -                          1                                                                    ⁢                                              τ                        _                                                                                                              2                                                    Trace              [                                                (                                      I                    -                                                                                            A                          ⁡                                                      (                                                                                                                            A                                  T                                                                ⁢                                A                                                            +                                                              γ                                ⁢                                                                                                                                  ⁢                                H                                                                                      )                                                                                                    -                          1                                                                    ⁢                                              A                        T                                                                              ]                                2                                                                        (        5        )            
Nob represents the number of discrete turbidity measurements. Simultaneous application of Equations 4 and 5 to the measured turbidity spectra yields the spherical-equivalent discretized particle size distribution.
Equations 1–5 can be used in a variety of ways depending on the information sought and the data available. For example, if the optical properties are known as functions of wavelength (i.e., the contribution from the chromophoric groups in the spectral region of interest), Equations 4–5 can be used to estimate the particle size distribution. Alternatively, if the particle size distribution is known (i.e. from microscopy, centrifugation, chromatography, sedimentation or other techniques), Equations 1, 2 and 4 can be used to estimate the optical properties, and therefore the chemical composition, of the particles and of the fluid of interest. Clearly, if the particles present have a variety of shapes, the optical properties obtained may not be completely accurate. Nevertheless, these effective optical property values are tantamount to a calibration and can be used to classify (fingerprint) and identify distinct particle and aggregate populations. This approach has been effectively applied to the analysis of platelets (see Mattley et. al, 2000) and for the characterization of bacterium vegetative cells and spores (see Alupoaei et al. 2002).